Fixing your data Swamp !!

Written By Alisdair Bach

Data Swamp, it’s a nice way to describe the mismanaged data and data assets created by complex connected organisations over the last three decades of industry 3.0……..

  • A data lake is a centralized repository where you store all your structured and unstructured data at any scale.

  • A data swamp is when that data lake becomes messy, disorganized, undocumented, and essentially unusable.


 Test yourself ! Signs of a Data Swamp:

  • No metadata or data cataloging (you don’t know what’s in there)

  • Poor or no governance

  • Redundant or outdated data

  • No clear ownership

  • Security and privacy risks due to lack of controls

  • Analysts and developers can’t find or trust the data

Causes:

  • Dumping raw data without structure or purpose

  • No data lifecycle management (old data just sits there)

  • Multiple teams uploading data inconsistently

Why It Matters $$$$:

A data swamp costs you loads in wasted tech investment, all those data products you purchased and never used properly !!!

And if data is the new gold, then your loosing competitive advantage through dross data, and don’t forget is not the quality of the data it’s how you access it, model it and cleanse it on the fly using AI, because AI loves bad data,,,,,,,

Then there is slow and patchy analytics, wasted storage, security and compliance issues. It defeats the purpose of having a data lake in the first place.

How to Fix a Data Swamp  

Here’s a practical breakdown from the Dragons:

1. Data Cataloging

  • Tag and document every data asset.

  • Tools: Collibra, Alation, Azure Purview, etc.

  • Add metadata so people know what the data is, where it came from, and how to use it.

2. Data Governance

  • Define data ownership: who’s responsible for quality, access, and lifecycle.

  • Apply data policies (e.g., retention, access control).

  • Enforce naming standards and structure.

3. Data Quality Management

  • Profile the data for issues (nulls, duplicates, format errors).

  • Clean or remove junk data.

  • Establish validation and enrichment processes.

4. Access Controls

  • Restrict access based on roles.

  • Monitor who is using what and how.

5. Lifecycle & Archiving

  • Define what data should be kept, archived, or deleted.

  • Don’t let obsolete data rot in place.

6. Centralized Stewardship

  • Set up a data stewardship program with business and IT involvement.


How a Data Fabric Helps

(We love Microsoft’s Data Fabric tooling, SAPs Business Data cloud is a work in progress and lacks the flexibility that hyperscaler offerings provide.)

A data fabric is virtual intelligent mesh that ties your enterprise wide data landscape together—across cloud, on-prem, and different systems.

It helps by:

  • Automating data discovery and cataloging using AI/ML

  • Unifying metadata to provide consistent data context

  • Making distributed data searchable and usable through virtualization

  • Applying governance and security consistently across environments

  • Accelerating data integration and self-service access

Key Tools and Vendors:

  • IBM Data Fabric

  • Talend Data Fabric

  • Informatica Intelligent Data Management Cloud

  • Microsoft’s Intelligent Data Platform (part of their data fabric story)

  • SAP Business Data Cloud

Final Thoughts

Fixing a data swamp is about organizing and governing the chaos, and AI just loves bringing order from chaos.

A data fabric is the data infrastructure and intelligence that helps you prevent a swamp from forming again—and makes your data accessible and trustworthy across the whole org.

If you need help making sense of your data strategy then speak to a Dragon.

Alisdair Bach
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